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  • SQL SERVER – Fundamentals of Columnstore Index

    - by pinaldave
    There are two kind of storage in database. Row Store and Column Store. Row store does exactly as the name suggests – stores rows of data on a page – and column store stores all the data in a column on the same page. These columns are much easier to search – instead of a query searching all the data in an entire row whether the data is relevant or not, column store queries need only to search much lesser number of the columns. This means major increases in search speed and hard drive use. Additionally, the column store indexes are heavily compressed, which translates to even greater memory and faster searches. I am sure this looks very exciting and it does not mean that you convert every single index from row store to column store index. One has to understand the proper places where to use row store or column store indexes. Let us understand in this article what is the difference in Columnstore type of index. Column store indexes are run by Microsoft’s VertiPaq technology. However, all you really need to know is that this method of storing data is columns on a single page is much faster and more efficient. Creating a column store index is very easy, and you don’t have to learn new syntax to create them. You just need to specify the keyword “COLUMNSTORE” and enter the data as you normally would. Keep in mind that once you add a column store to a table, though, you cannot delete, insert or update the data – it is READ ONLY. However, since column store will be mainly used for data warehousing, this should not be a big problem. You can always use partitioning to avoid rebuilding the index. A columnstore index stores each column in a separate set of disk pages, rather than storing multiple rows per page as data traditionally has been stored. The difference between column store and row store approaches is illustrated below: In case of the row store indexes multiple pages will contain multiple rows of the columns spanning across multiple pages. In case of column store indexes multiple pages will contain multiple single columns. This will lead only the columns needed to solve a query will be fetched from disk. Additionally there is good chance that there will be redundant data in a single column which will further help to compress the data, this will have positive effect on buffer hit rate as most of the data will be in memory and due to same it will not need to be retrieved. Let us see small example of how columnstore index improves the performance of the query on a large table. As a first step let us create databaseset which is large enough to show performance impact of columnstore index. The time taken to create sample database may vary on different computer based on the resources. USE AdventureWorks GO -- Create New Table CREATE TABLE [dbo].[MySalesOrderDetail]( [SalesOrderID] [int] NOT NULL, [SalesOrderDetailID] [int] NOT NULL, [CarrierTrackingNumber] [nvarchar](25) NULL, [OrderQty] [smallint] NOT NULL, [ProductID] [int] NOT NULL, [SpecialOfferID] [int] NOT NULL, [UnitPrice] [money] NOT NULL, [UnitPriceDiscount] [money] NOT NULL, [LineTotal] [numeric](38, 6) NOT NULL, [rowguid] [uniqueidentifier] NOT NULL, [ModifiedDate] [datetime] NOT NULL ) ON [PRIMARY] GO -- Create clustered index CREATE CLUSTERED INDEX [CL_MySalesOrderDetail] ON [dbo].[MySalesOrderDetail] ( [SalesOrderDetailID]) GO -- Create Sample Data Table -- WARNING: This Query may run upto 2-10 minutes based on your systems resources INSERT INTO [dbo].[MySalesOrderDetail] SELECT S1.* FROM Sales.SalesOrderDetail S1 GO 100 Now let us do quick performance test. I have kept STATISTICS IO ON for measuring how much IO following queries take. In my test first I will run query which will use regular index. We will note the IO usage of the query. After that we will create columnstore index and will measure the IO of the same. -- Performance Test -- Comparing Regular Index with ColumnStore Index USE AdventureWorks GO SET STATISTICS IO ON GO -- Select Table with regular Index SELECT ProductID, SUM(UnitPrice) SumUnitPrice, AVG(UnitPrice) AvgUnitPrice, SUM(OrderQty) SumOrderQty, AVG(OrderQty) AvgOrderQty FROM [dbo].[MySalesOrderDetail] GROUP BY ProductID ORDER BY ProductID GO -- Table 'MySalesOrderDetail'. Scan count 1, logical reads 342261, physical reads 0, read-ahead reads 0. -- Create ColumnStore Index CREATE NONCLUSTERED COLUMNSTORE INDEX [IX_MySalesOrderDetail_ColumnStore] ON [MySalesOrderDetail] (UnitPrice, OrderQty, ProductID) GO -- Select Table with Columnstore Index SELECT ProductID, SUM(UnitPrice) SumUnitPrice, AVG(UnitPrice) AvgUnitPrice, SUM(OrderQty) SumOrderQty, AVG(OrderQty) AvgOrderQty FROM [dbo].[MySalesOrderDetail] GROUP BY ProductID ORDER BY ProductID GO It is very clear from the results that query is performance extremely fast after creating ColumnStore Index. The amount of the pages it has to read to run query is drastically reduced as the column which are needed in the query are stored in the same page and query does not have to go through every single page to read those columns. If we enable execution plan and compare we can see that column store index performance way better than regular index in this case. Let us clean up the database. -- Cleanup DROP INDEX [IX_MySalesOrderDetail_ColumnStore] ON [dbo].[MySalesOrderDetail] GO TRUNCATE TABLE dbo.MySalesOrderDetail GO DROP TABLE dbo.MySalesOrderDetail GO In future posts we will see cases where Columnstore index is not appropriate solution as well few other tricks and tips of the columnstore index. Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: Pinal Dave, PostADay, SQL, SQL Authority, SQL Index, SQL Optimization, SQL Performance, SQL Query, SQL Scripts, SQL Server, SQL Tips and Tricks, T SQL, Technology

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  • An OLAP client!

    - by Davide Mauri
    While surfing CodePlex I’ve come across a very interesting tool for all BI Developers who misses a decent OLAP client where to write, run & test MDX queries http://ranetuilibraryolap.codeplex.com/ I’ve not tested it yet, but I’ll surely do this week and I’ll post my impressions ASAP. The first impression, just looking the CodePlex page, is that tool Rocks!!!!! Share this post: email it! | bookmark it! | digg it! | reddit! | kick it! | live it!

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  • SQL SERVER – SSMS: Top Object and Batch Execution Statistics Reports

    - by Pinal Dave
    The month of June till mid of July has been the fever of sports. First, it was Wimbledon Tennis and then the Soccer fever was all over. There is a huge number of fan followers and it is great to see the level at which people sometimes worship these sports. Being an Indian, I cannot forget to mention the India tour of England later part of July. Following these sports and as the events unfold to the finals, there are a number of ways the statisticians can slice and dice the numbers. Cue from soccer I can surely say there is a team performance against another team and then there is individual member fairs against a particular opponent. Such statistics give us a fair idea to how a team in the past or in the recent past has fared against each other, head-to-head stats during World cup and during other neutral venue games. All these statistics are just pointers. In reality, they don’t reflect the calibre of the current team because the individuals who performed in each of these games are totally different (Typical example being the Brazil Vs Germany semi-final match in FIFA 2014). So at times these numbers are misleading. It is worth investigating and get the next level information. Similar to these statistics, SQL Server Management studio is also equipped with a number of reports like a) Object Execution Statistics report and b) Batch Execution Statistics reports. As discussed in the example, the team scorecard is like the Batch Execution statistics and individual stats is like Object Level statistics. The analogy can be taken only this far, trust me there is no correlation between SQL Server functioning and playing sports – It is like I think about diet all the time except while I am eating. Performance – Batch Execution Statistics Let us view the first report which can be invoked from Server Node -> Reports -> Standard Reports -> Performance – Batch Execution Statistics. Most of the values that are displayed in this report come from the DMVs sys.dm_exec_query_stats and sys.dm_exec_sql_text(sql_handle). This report contains 3 distinctive sections as outline below.   Section 1: This is a graphical bar graph representation of Average CPU Time, Average Logical reads and Average Logical Writes for individual batches. The Batch numbers are indicative and the details of individual batch is available in section 3 (detailed below). Section 2: This represents a Pie chart of all the batches by Total CPU Time (%) and Total Logical IO (%) by batches. This graphical representation tells us which batch consumed the highest CPU and IO since the server started, provided plan is available in the cache. Section 3: This is the section where we can find the SQL statements associated with each of the batch Numbers. This also gives us the details of Average CPU / Average Logical Reads and Average Logical Writes in the system for the given batch with object details. Expanding the rows, I will also get the # Executions and # Plans Generated for each of the queries. Performance – Object Execution Statistics The second report worth a look is Object Execution statistics. This is a similar report as the previous but turned on its head by SQL Server Objects. The report has 3 areas to look as above. Section 1 gives the Average CPU, Average IO bar charts for specific objects. The section 2 is a graphical representation of Total CPU by objects and Total Logical IO by objects. The final section details the various objects in detail with the Avg. CPU, IO and other details which are self-explanatory. At a high-level both the reports are based on queries on two DMVs (sys.dm_exec_query_stats and sys.dm_exec_sql_text) and it builds values based on calculations using columns in them: SELECT * FROM    sys.dm_exec_query_stats s1 CROSS APPLY sys.dm_exec_sql_text(sql_handle) AS s2 WHERE   s2.objectid IS NOT NULL AND DB_NAME(s2.dbid) IS NOT NULL ORDER BY  s1.sql_handle; This is one of the simplest form of reports and in future blogs we will look at more complex reports. I truly hope that these reports can give DBAs and developers a hint about what is the possible performance tuning area. As a closing point I must emphasize that all above reports pick up data from the plan cache. If a particular query has consumed a lot of resources earlier, but plan is not available in the cache, none of the above reports would show that bad query. Reference: Pinal Dave (http://blog.sqlauthority.com)Filed under: SQL, SQL Authority, SQL Query, SQL Server, SQL Server Management Studio, SQL Tips and Tricks, T SQL Tagged: SQL Reports

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  • Storing large data in HTTP Session (Java Application)

    - by Umesh Awasthi
    I am asking this question in continuation with http-session-or-database-approach. I am planning to follow this approach. When user add product to cart, create a Cart Model, add items to cart and save to DB. Convert Cart model to cart data and save it to HTTP session. Any update/ edit update underlying cart in DB and update data snap shot in Session. When user click on view cart page, just pick cart data from Session and display to customer. I have following queries regarding HTTP Session How good is it to store large data (Shopping Cart) in Session? How scalable this approach can be ? (With respect to Session) Won't my application going to eat and demand a lot of memory? Is my approach is fine or do i need to consider other points while designing this? Though, we can control what all cart data should be stored in the Session, but still we need to have certain information in cart data being stored in session?

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  • 301 redirect bulk aspx URLs on IIS

    - by tiki16
    We recently relaunched an old ASPX site as a new Drupal site on the same domain. No 301 redirect was implemented. I have outputted a list of 1000 URLs that need to be 301 redirected. Most of the URLs are the results of search queries that were committed on the website. I.E.: http://www.mysite.com/electronics/CommunityDetails.aspx?FirstLetter=%&ID=444 We are running a Drupal site on IIS using a PHP plugin. Is there a way I can wild card a redirect of all ASPX pages? I know I can do it with .htaccess but that doesn't apply here. Any suggestions appreciated.

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  • Registrar with good security, DNS hosting, and DNSSEC and IPv6 resolvers?

    - by semenko
    I'm looking to move my domains away from GoDaddy, but I'm having a tough time finding anyone with comparable features at a (even remotely) similar price. I've looked at the usual suggestions (NameCheap, Gandi.net, etc.), but they all seem to lack many of the GoDaddy feature base. I'm looking for: DNSSEC IPv6 Resolvers (dig pdns01.domaincontrol.com AAAA; etc. ) SSL-Logins by default HTTP-only login cookies No stupid password restrictions Two-factor authentications No DNS record limits Rough DNS statistics (queries/day, etc.) Audit trails GoDaddy has all of these, except two-factor, for $3/month. See http://www.godaddy.com/domains/dns-hosting.aspx I can't seem to find any other registrar that supports even a few of these. Is there a registrar that offers comparable features? Or, barring that, a DNS hosting service that offers similar features? (AWS Route53 doesn't offer DNSSEC or IPv6)

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  • Retrieve Performance Data from SOA Infrastructure Database

    - by fip
    My earlier blog posting shows how to enable, retrieve and interpret BPEL engine performance statistics to aid performance troubleshooting. The strength of BPEL engine statistics at EM is its break down per request. But there are some limitations with the BPEL performance statistics mentioned in that blog posting: The statistics were stored in memory instead of being persisted. To avoid memory overflow, the data are stored to a buffer with limited size. When the statistic entries exceed the limitation, old data will be flushed out to give ways to new statistics. Therefore it can only keep the last X number of entries of data. The statistics 5 hour ago may not be there anymore. The BPEL engine performance statistics only includes latencies. It does not provide throughputs. Fortunately, Oracle SOA Suite runs with the SOA Infrastructure database and a lot of performance data are naturally persisted there. It is at a more coarse grain than the in-memory BPEL Statistics, but it does have its own strengths as it is persisted. Here I would like offer examples of some basic SQL queries you can run against the infrastructure database of Oracle SOA Suite 11G to acquire the performance statistics for a given period of time. You can run it immediately after you modify the date range to match your actual system. 1. Asynchronous/one-way messages incoming rates The following query will show number of messages sent to one-way/async BPEL processes during a given time period, organized by process names and states select composite_name composite, state, count(*) Count from dlv_message where receive_date >= to_timestamp('2012-10-24 21:00:00','YYYY-MM-DD HH24:MI:SS') and receive_date <= to_timestamp('2012-10-24 21:59:59','YYYY-MM-DD HH24:MI:SS') group by composite_name, state order by Count; 2. Throughput of BPEL process instances The following query shows the number of synchronous and asynchronous process instances created during a given time period. It list instances of all states, including the unfinished and faulted ones. The results will include all composites cross all SOA partitions select state, count(*) Count, composite_name composite, component_name,componenttype from cube_instance where creation_date >= to_timestamp('2012-10-24 21:00:00','YYYY-MM-DD HH24:MI:SS') and creation_date <= to_timestamp('2012-10-24 21:59:59','YYYY-MM-DD HH24:MI:SS') group by composite_name, component_name, componenttype order by count(*) desc; 3. Throughput and latencies of BPEL process instances This query is augmented on the previous one, providing more comprehensive information. It gives not only throughput but also the maximum, minimum and average elapse time BPEL process instances. select composite_name Composite, component_name Process, componenttype, state, count(*) Count, trunc(Max(extract(day from (modify_date-creation_date))*24*60*60 + extract(hour from (modify_date-creation_date))*60*60 + extract(minute from (modify_date-creation_date))*60 + extract(second from (modify_date-creation_date))),4) MaxTime, trunc(Min(extract(day from (modify_date-creation_date))*24*60*60 + extract(hour from (modify_date-creation_date))*60*60 + extract(minute from (modify_date-creation_date))*60 + extract(second from (modify_date-creation_date))),4) MinTime, trunc(AVG(extract(day from (modify_date-creation_date))*24*60*60 + extract(hour from (modify_date-creation_date))*60*60 + extract(minute from (modify_date-creation_date))*60 + extract(second from (modify_date-creation_date))),4) AvgTime from cube_instance where creation_date >= to_timestamp('2012-10-24 21:00:00','YYYY-MM-DD HH24:MI:SS') and creation_date <= to_timestamp('2012-10-24 21:59:59','YYYY-MM-DD HH24:MI:SS') group by composite_name, component_name, componenttype, state order by count(*) desc;   4. Combine all together Now let's combine all of these 3 queries together, and parameterize the start and end time stamps to make the script a bit more robust. The following script will prompt for the start and end time before querying against the database: accept startTime prompt 'Enter start time (YYYY-MM-DD HH24:MI:SS)' accept endTime prompt 'Enter end time (YYYY-MM-DD HH24:MI:SS)' Prompt "==== Rejected Messages ===="; REM 2012-10-24 21:00:00 REM 2012-10-24 21:59:59 select count(*), composite_dn from rejected_message where created_time >= to_timestamp('&&StartTime','YYYY-MM-DD HH24:MI:SS') and created_time <= to_timestamp('&&EndTime','YYYY-MM-DD HH24:MI:SS') group by composite_dn; Prompt " "; Prompt "==== Throughput of one-way/asynchronous messages ===="; select state, count(*) Count, composite_name composite from dlv_message where receive_date >= to_timestamp('&StartTime','YYYY-MM-DD HH24:MI:SS') and receive_date <= to_timestamp('&EndTime','YYYY-MM-DD HH24:MI:SS') group by composite_name, state order by Count; Prompt " "; Prompt "==== Throughput and latency of BPEL process instances ====" select state, count(*) Count, trunc(Max(extract(day from (modify_date-creation_date))*24*60*60 + extract(hour from (modify_date-creation_date))*60*60 + extract(minute from (modify_date-creation_date))*60 + extract(second from (modify_date-creation_date))),4) MaxTime, trunc(Min(extract(day from (modify_date-creation_date))*24*60*60 + extract(hour from (modify_date-creation_date))*60*60 + extract(minute from (modify_date-creation_date))*60 + extract(second from (modify_date-creation_date))),4) MinTime, trunc(AVG(extract(day from (modify_date-creation_date))*24*60*60 + extract(hour from (modify_date-creation_date))*60*60 + extract(minute from (modify_date-creation_date))*60 + extract(second from (modify_date-creation_date))),4) AvgTime, composite_name Composite, component_name Process, componenttype from cube_instance where creation_date >= to_timestamp('&StartTime','YYYY-MM-DD HH24:MI:SS') and creation_date <= to_timestamp('&EndTime','YYYY-MM-DD HH24:MI:SS') group by composite_name, component_name, componenttype, state order by count(*) desc;  

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  • Iron Speed Designer Review

    While Visual Studio allows developers to get productive fast by providing great design tools for a UI, it still lacks the ability to do smart layouts, data connections and queries. It is in this area that RAD suite of applications can tremendously boost productivity by abstracting away some of these issues and saving developer time to focus on business intelligence instead of data extraction and presentation. When it comes to RAD application suites for managed web applications, there is non better than Iron Speed Designer. The ease with which you can create a data-centric web application and have different reports of your data within minutes are unparalleled. This review delves into what Iron Speed Designer has to offer as well as some of its limitations. Iron Speed works with .NET 2.0, 3.0, 3.5 and even the latest version .NET 4.0. Read More >

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  • A message to Denis Pitcher

    Denis Pitcher, You posted this comment on my blog and some other blogs: Devteach's promotion for a one year MSDN subscription was not honoured and attempts to contact them result in a "we sent attendee info to MS, it's not our problem" response while attempts to contact Microsoft result in the suggestion that any queries should be redirect to Devteach. Hopefully not all attendees we're cheated though if you're considering attending a future Devteach it is recommended that you don't...Did you know that DotNetSlackers also publishes .net articles written by top known .net Authors? We already have over 80 articles in several categories including Silverlight. Take a look: here.

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  • Responsible BI for Excel, Even for Older Versions

    - by andrewbrust
    On Wednesday, I will have the honor of co-presenting, for both The Data Warehouse Institute (TDWI) and the New York Technology Council. on the subject of Excel and BI. My co-presenter will be none other than Bill Baker, who was a Microsoft Distinguished Engineer and, essentially, the father of BI at that company.  Details on the events are here and here. We'll be talking about PowerPivot, of course, but that's not all. Probably even more important than any one product, will be our discussion of whether the usual characterization of Excel as the nemesis of IT, the guilty pleasure of business users and the antithesis of formal BI is really valid and/or hopelessly intractable. Without giving away our punchline, I'll tell you that we are much more optimistic than that. There are huge upsides to Excel and while there are real dangers to using it in the BI space, there are standards and practices you can employ to ensure Excel is used responsibly. And when those practices are followed, Excel becomes quite powerful indeed. One of the keys to this is using Excel as a data consumer rather than data storage mechanism. Caching data in Excel is OK, but only if that data is (a) not modified and (b) configured for automated periodic refresh. PowerPivot meets both criteria -- it stores a read-only copy of your data in the form of a model, and once workbook containing a PowerPivot model is published to SharePoint, it can be configured for scheduled data refresh, on the server, requiring no user intervention whatsoever. Data refresh is a bit like hard drive backup: it will only happen reliably if it's automated, and super-easy to configure. PowerPivot hits a real home run here (as does Windows Home Server for PC backup, but I digress). The thing about PowerPivot is that it's an add-in for Excel 2010. What if you're not planning to go to that new version for quite a while? What if you’ve just deployed Office 2007 in your organization? What if you're still on Office 2003, or an even earlier version? What can you do immediately to share data responsibly and easily? As it turns out, there's a feature in Excel that's been around for quite a while, that can help: Web Queries.  The Web Query feature was introduced, ostensibly, to allow Excel to pull data in from Internet Web pages…for example, data in a stock quote history table will come in nicely, as will any data in a Web page that is displayed in an HTML table.  To use the feature In Excel 2007 or 2010, click the Data Tab or the ribbon and click the “From Web” button towards the left; in older versions use the corresponding option in  the menu or  toolbars.  Next, paste a URL into the resulting dialog box and tap Enter or click the Go button.  A preview of the Web page will come up, and the dialog will allow you to select the specific table within the page whose data you’d like to import.  Here’s an example: Now just click the table, click the Import button, and the Import Data dialog appears.  You can simply click OK to bring in your data or you can first click the Properties… button and configure the data import to be refreshed at an interval in minutes that you select.  Now your data’s in the spreadsheet and ready to worked with: Your data may be vulnerable to modification, but if you’ve set up the data refresh, any accidental or malicious changes will be corrected in time anyway. The thing about this feature is that it’s most useful not for public Web pages, but for pages behind the firewall.  In effect, the Web Query feature provides an incredibly easy way to consume data in Excel that’s “published” from an application.  Users just need a URL.  They don’t need to know server and database names and since the data is read-only, providing credentials may be unnecessary, or can be handled using integrated security.  If that’s not good enough, the Web Query can be saved to a special .iqy file, which can be edited to provide POST parameter data. The only requirement is that the data must be provided in an HTML table, with the first row providing the column names.  From an ASP.NET project, it couldn’t be easier: a simple bound GridView control is totally compatible.  Use a data source control with it, and you don’t even have to write any code.  Users can link to pages that are part of an application’s UI, or developers can create pages that are specially designed for the purpose of providing an interface to the Web Query import feature.  And none of this is Microsoft- or .NET-specific.  You can create pages in any language you want (PHP comes to mind) that output the result set of a query in HTML table format, and then consume that data in a Web Query.  Then build PivotTables and charts on the data, and in Excel 2007 or 2010 you can use conditional formatting to create scorecards and dashboards. This strategy allows you to create pages that function quite similarly to the OData XML feeds rendered when .NET developers create an “Astoria” WCF Data Service.  And while it’s cool that PowerPivot and Excel 2010 can import such OData feeds, it’s good to know that older versions of Excel can function in a similar fashion, and can consume data produced by virtually any Web development platform. As a final matter, instead of just telling you that “older versions” of Excel support this feature, I’ll be more specific.  To discover what the first version of Excel was to support Web queries, go to http://bit.ly/OldSchoolXL.

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  • Predicting advantages of database denormalization

    - by Janus Troelsen
    I was always taught to strive for the highest Normal Form of database normalization, and we were taught Bernstein's Synthesis algorithm to achieve 3NF. This is all very well and it feels nice to normalize your database, knowing that fields can be modified while retaining consistency. However, performance may suffer. That's why I am wondering whether there is any way to predict the speedup/slowdown when denormalizing. That way, you can build your list of FD's featuring 3NF and then denormalize as little as possible. I imagine that denormalizing too much would waste space and time, because e.g. giant blobs are duplicated or it because harder to maintain consistency because you have to update multiple fields using a transaction. Summary: Given a 3NF FD set, and a set of queries, how do I predict the speedup/slowdown of denormalization? Link to papers appreciated too.

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  • Resolving a cname using different DNS

    - by Sandeep Singh Rawat
    I have a domain name (e.g. abc.com) registered in GoDaddy and I have a few subdomains (mail, blog) correctly setup to a different hosts. Now I want to park my domain with a parking host (seohosting.com) which asked me to change my nameserver to their DNS. What I want is to only redirect dns queries for (www or @) cname to seohosting.com while still being able to use my other cname for my own purpose. Is there a way to do this? I dont have the host IP address for parking host.

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  • Logical and Physical Modeling for Analytical Applications

    - by Dejan Sarka
    I am proud to announce that my first course for Pluralsight is released. The course title is Logical and Physical Modeling for Analytical Applications. Here is the description of the course. A bad data model leads to an application that does not perform well. Therefore, when developing an application, you should create a good data model from the start. However, even the best logical model can’t help when the physical implementation is bad. It is also important to know how SQL Server stores and accesses data, and how to optimize the data access. Database optimization starts by splitting transactional and analytical applications. In this course, you learn how to support analytical applications with logical design, get understanding of the problems with data access for queries that deal with large amounts of data, and learn about SQL Server optimizations that help solving these problems. Enjoy the course!

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  • Disable default error pages/error messages in IIS

    - by Antoine
    I have this web application (ASP.Net MVC 3) that on certain conditions returns a custom JSON string with a HTTP status code for an error (403, 415, 500). It is deployed on a Win 2008 R2 server with IIS 7.5 Initially I was gettting the standard HTML pages for the error instead of the JSON data. I removed the error pages for these errors in the app settings. But now my queries which should return some JSON data return a single error line. When the server gives me 403, I have the message "You do not have permission to view this directory or page." (simple line, no HTML around it). What can I do to deactivate this and finally get what the app is returning and not what the server wants to return?

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  • Using Cloud OER to Find Fusion Applications On-Premise Service Concrete WSDL URL by Rajesh Raheja

    - by JuergenKress
    In my previous post on Fusion Applications Integration, the Fusion Applications OER white paper explains Oracle Enterprise Repository (OER) usage in the applications context, assuming a dedicated OER for your Fusion Applications instance (whether cloud/SaaS or on-premise). Having a dedicated OER instance is recommended as it can provide customized service metadata and can be used for overall SOA governance in addition to simple service discovery. One of the common queries I get is how on-premise customers without a dedicated OER can find a concrete service WSDL URL for their specific environment using the cloud hosted OER instance. Read the full article here. SOA & BPM Partner Community For regular information on Oracle SOA Suite become a member in the SOA & BPM Partner Community for registration please visit  www.oracle.com/goto/emea/soa (OPN account required) If you need support with your account please contact the Oracle Partner Business Center. Blog Twitter LinkedIn Mix Forum Technorati Tags: OER,SOA Governance,SOA Community,Oracle SOA,Oracle BPM,BPM,Community,OPN,Jürgen Kress

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  • MySQL Connect Only 10 Days Away - Focus on InnoDB Sessions

    - by Bertrand Matthelié
    Time flies and MySQL Connect is only 10 days away! You can check out the full program here as well as in the September edition of the MySQL newsletter. Mat recently blogged about the MySQL Cluster sessions you’ll have the opportunity to attend, and below are those focused on InnoDB. Remember you can plan your schedule with Schedule Builder. Saturday, 1.00 pm, Room Golden Gate 3: 10 Things You Should Know About InnoDB—Calvin Sun, Oracle InnoDB is the default storage engine for Oracle’s MySQL as of MySQL Release 5.5. It provides the standard ACID-compliant transactions, row-level locking, multiversion concurrency control, and referential integrity. InnoDB also implements several innovative technologies to improve its performance and reliability. This presentation gives a brief history of InnoDB; its main features; and some recent enhancements for better performance, scalability, and availability. Saturday, 5.30 pm, Room Golden Gate 4: Demystified MySQL/InnoDB Performance Tuning—Dimitri Kravtchuk, Oracle This session covers performance tuning with MySQL and the InnoDB storage engine for MySQL and explains the main improvements made in MySQL Release 5.5 and Release 5.6. Which setting for which workload? Which value will be better for my system? How can I avoid potential bottlenecks from the beginning? Do I need a purge thread? Is it true that InnoDB doesn't need thread concurrency anymore? These and many other questions are asked by DBAs and developers. Things are changing quickly and constantly, and there is no “silver bullet.” But understanding the configuration setting’s impact is already a huge step in performance improvement. Bring your ideas and problems to share them with others—the discussion is open, just moderated by a speaker. Sunday, 10.15 am, Room Golden Gate 4: Better Availability with InnoDB Online Operations—Calvin Sun, Oracle Many top Web properties rely on Oracle’s MySQL as a critical piece of infrastructure for serving millions of users. Database availability has become increasingly important. One way to enhance availability is to give users full access to the database during data definition language (DDL) operations. The online DDL operations in recent MySQL releases offer users the flexibility to perform schema changes while having full access to the database—that is, with minimal delay of operations on a table and without rebuilding the entire table. These enhancements provide better responsiveness and availability in busy production environments. This session covers these improvements in the InnoDB storage engine for MySQL for online DDL operations such as add index, drop foreign key, and rename column. Sunday, 11.45 am, Room Golden Gate 7: Developing High-Throughput Services with NoSQL APIs to InnoDB and MySQL Cluster—Andrew Morgan and John Duncan, Oracle Ever-increasing performance demands of Web-based services have generated significant interest in providing NoSQL access methods to MySQL (MySQL Cluster and the InnoDB storage engine of MySQL), enabling users to maintain all the advantages of their existing relational databases while providing blazing-fast performance for simple queries. Get the best of both worlds: persistence; consistency; rich SQL queries; high availability; scalability; and simple, flexible APIs and schemas for agile development. This session describes the memcached connectors and examines some use cases for how MySQL and memcached fit together in application architectures. It does the same for the newest MySQL Cluster native connector, an easy-to-use, fully asynchronous connector for Node.js. Sunday, 1.15 pm, Room Golden Gate 4: InnoDB Performance Tuning—Inaam Rana, Oracle The InnoDB storage engine has always been highly efficient and includes many unique architectural elements to ensure high performance and scalability. In MySQL 5.5 and MySQL 5.6, InnoDB includes many new features that take better advantage of recent advances in operating systems and hardware platforms than previous releases did. This session describes unique InnoDB architectural elements for performance, new features, and how to tune InnoDB to achieve better performance. Sunday, 4.15 pm, Room Golden Gate 3: InnoDB Compression for OLTP—Nizameddin Ordulu, Facebook and Inaam Rana, Oracle Data compression is an important capability of the InnoDB storage engine for Oracle’s MySQL. Compressed tables reduce the size of the database on disk, resulting in fewer reads and writes and better throughput by reducing the I/O workload. Facebook pushes the limit of InnoDB compression and has made several enhancements to InnoDB, making this technology ready for online transaction processing (OLTP). In this session, you will learn the fundamentals of InnoDB compression. You will also learn the enhancements the Facebook team has made to improve InnoDB compression, such as reducing compression failures, not logging compressed page images, and allowing changes of compression level. Not registered yet? You can still save US$ 300 over the on-site fee – Register Now!

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  • Eager Loading more than 1 table in LinqtoSql

    - by Michael Freidgeim
    When I've tried in Linq2Sql to load table with 2 child tables, I've noticed, that multiple SQLs are generated. I've found that  it isa known issue, if you try to specify more than one to pre-load it just  picks which one to pre-load and which others to leave deferred (simply ignoring those LoadWith hints)There are more explanations in http://codebetter.com/blogs/david.hayden/archive/2007/08/06/linq-to-sql-query-tuning-appears-to-break-down-in-more-advanced-scenarios.aspxThe reason the relationship in your blog post above is generating multiple queries is that you have two (1:n) relationship (Customers->Orders) and (Orders->OrderDetails). If you just had one (1:n) relationship (Customer->Orders) or (Orders->OrderDetails) LINQ to SQL would optimize and grab it in one query (using a JOIN).  The alternative -to use SQL and POCO classes-see http://stackoverflow.com/questions/238504/linq-to-sql-loading-child-entities-without-using-dataloadoptions?rq=1Fortunately the problem is not applicable to Entity Framework, that we want to use in future development instead of Linq2SqlProduct firstProduct = db.Product.Include("OrderDetail").Include("Supplier").First(); ?

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  • Redehost Transforms Cloud & Hosting Services with MySQL Enterprise Edition

    - by Mat Keep
    RedeHost are one of Brazil's largest cloud computing and web hosting providers, with more than 60,000 customers and 52,000 web sites running on its infrastructure. As the company grew, Redehost needed to automate operations, such as system monitoring, making the operations team more proactive in solving problems. Redehost also sought to improve server uptime, robustness, and availability, especially during backup windows, when performance would often dip. To address the needs of the business, Redehost migrated from the community edition of MySQL to MySQL Enterprise Edition, which has delivered a host of benefits: - Pro-active database management and monitoring using MySQL Enterprise Monitor, enabling Redehost to fulfil customer SLAs. Using the Query Analyzer, Redehost were able to more rapidly identify slow queries, improving customer support - Quadrupled backup speed with MySQL Enterprise Backup, leading to faster data recovery and improved system availability - Reduced DBA overhead by 50% due to the improved support capabilities offered by MySQL Enterprise Edition. - Enabled infrastructure consolidation, avoiding unnecessary energy costs and premature hardware acquisition You can learn more from the full Redehost Case Study Also, take a look at the recently updated MySQL in the Cloud whitepaper for the latest developments that are making it even simpler and more efficient to develop and deploy new services with MySQL in the cloud

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  • Forward Mysql logs to Syslog-ng

    - by Mbeale
    Ubuntu 10.04 I have set MySQL to log slow queries and a general mysql log. How can I pipe those files in syslog to forward to centralized logging service (which is working)? Tried: source s_mysql_instance_1 { pipe("/var/log/mysql/mysql.log" ); }; log { source(s_mysql_instance_1); destination(d_loggly); }; Get: Error opening file for reading; filename='/var/log/mysql/mysql.log', error='Permission denied (13)' Error initializing source driver; source='s_mysql_instance_1', id='s_mysql_instance_1#0' Error initializing message pipeline; I have also disabled apparmor and still get the same results

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  • Getting started with Oracle Database In-Memory Part III - Querying The IM Column Store

    - by Maria Colgan
    In my previous blog posts, I described how to install, enable, and populate the In-Memory column store (IM column store). This weeks post focuses on how data is accessed within the IM column store. Let’s take a simple query “What is the most expensive air-mail order we have received to date?” SELECT Max(lo_ordtotalprice) most_expensive_order FROM lineorderWHERE  lo_shipmode = 5; The LINEORDER table has been populated into the IM column store and since we have no alternative access paths (indexes or views) the execution plan for this query is a full table scan of the LINEORDER table. You will notice that the execution plan has a new set of keywords “IN MEMORY" in the access method description in the Operation column. These keywords indicate that the LINEORDER table has been marked for INMEMORY and we may use the IM column store in this query. What do I mean by “may use”? There are a small number of cases were we won’t use the IM column store even though the object has been marked INMEMORY. This is similar to how the keyword STORAGE is used on Exadata environments. You can confirm that the IM column store was actually used by examining the session level statistics, but more on that later. For now let's focus on how the data is accessed in the IM column store and why it’s faster to access the data in the new column format, for analytical queries, rather than the buffer cache. There are four main reasons why accessing the data in the IM column store is more efficient. 1. Access only the column data needed The IM column store only has to scan two columns – lo_shipmode and lo_ordtotalprice – to execute this query while the traditional row store or buffer cache has to scan all of the columns in each row of the LINEORDER table until it reaches both the lo_shipmode and the lo_ordtotalprice column. 2. Scan and filter data in it's compressed format When data is populated into the IM column it is automatically compressed using a new set of compression algorithms that allow WHERE clause predicates to be applied against the compressed formats. This means the volume of data scanned in the IM column store for our query will be far less than the same query in the buffer cache where it will scan the data in its uncompressed form, which could be 20X larger. 3. Prune out any unnecessary data within each column The fastest read you can execute is the read you don’t do. In the IM column store a further reduction in the amount of data accessed is possible due to the In-Memory Storage Indexes(IM storage indexes) that are automatically created and maintained on each of the columns in the IM column store. IM storage indexes allow data pruning to occur based on the filter predicates supplied in a SQL statement. An IM storage index keeps track of minimum and maximum values for each column in each of the In-Memory Compression Unit (IMCU). In our query the WHERE clause predicate is on the lo_shipmode column. The IM storage index on the lo_shipdate column is examined to determine if our specified column value 5 exist in any IMCU by comparing the value 5 to the minimum and maximum values maintained in the Storage Index. If the value 5 is outside the minimum and maximum range for an IMCU, the scan of that IMCU is avoided. For the IMCUs where the value 5 does fall within the min, max range, an additional level of data pruning is possible via the metadata dictionary created when dictionary-based compression is used on IMCU. The dictionary contains a list of the unique column values within the IMCU. Since we have an equality predicate we can easily determine if 5 is one of the distinct column values or not. The combination of the IM storage index and dictionary based pruning, enables us to only scan the necessary IMCUs. 4. Use SIMD to apply filter predicates For the IMCU that need to be scanned Oracle takes advantage of SIMD vector processing (Single Instruction processing Multiple Data values). Instead of evaluating each entry in the column one at a time, SIMD vector processing allows a set of column values to be evaluated together in a single CPU instruction. The column format used in the IM column store has been specifically designed to maximize the number of column entries that can be loaded into the vector registers on the CPU and evaluated in a single CPU instruction. SIMD vector processing enables the Oracle Database In-Memory to scan billion of rows per second per core versus the millions of rows per second per core scan rate that can be achieved in the buffer cache. I mentioned earlier in this post that in order to confirm the IM column store was used; we need to examine the session level statistics. You can monitor the session level statistics by querying the performance views v$mystat and v$statname. All of the statistics related to the In-Memory Column Store begin with IM. You can see the full list of these statistics by typing: display_name format a30 SELECT display_name FROM v$statname WHERE  display_name LIKE 'IM%'; If we check the session statistics after we execute our query the results would be as follow; SELECT Max(lo_ordtotalprice) most_expensive_order FROM lineorderWHERE lo_shipmode = 5; SELECT display_name FROM v$statname WHERE  display_name IN ('IM scan CUs columns accessed',                        'IM scan segments minmax eligible',                        'IM scan CUs pruned'); As you can see, only 2 IMCUs were accessed during the scan as the majority of the IMCUs (44) in the LINEORDER table were pruned out thanks to the storage index on the lo_shipmode column. In next weeks post I will describe how you can control which queries use the IM column store and which don't. +Maria Colgan

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  • NoSQL Java API for MySQL Cluster: Questions & Answers

    - by Mat Keep
    The MySQL Cluster engineering team recently ran a live webinar, available now on-demand demonstrating the ClusterJ and ClusterJPA NoSQL APIs for MySQL Cluster, and how these can be used in building real-time, high scale Java-based services that require continuous availability. Attendees asked a number of great questions during the webinar, and I thought it would be useful to share those here, so others are also able to learn more about the Java NoSQL APIs. First, a little bit about why we developed these APIs and why they are interesting to Java developers. ClusterJ and Cluster JPA ClusterJ is a Java interface to MySQL Cluster that provides either a static or dynamic domain object model, similar to the data model used by JDO, JPA, and Hibernate. A simple API gives users extremely high performance for common operations: insert, delete, update, and query. ClusterJPA works with ClusterJ to extend functionality, including - Persistent classes - Relationships - Joins in queries - Lazy loading - Table and index creation from object model By eliminating data transformations via SQL, users get lower data access latency and higher throughput. In addition, Java developers have a more natural programming method to directly manage their data, with a complete, feature-rich solution for Object/Relational Mapping. As a result, the development of Java applications is simplified with faster development cycles resulting in accelerated time to market for new services. MySQL Cluster offers multiple NoSQL APIs alongside Java: - Memcached for a persistent, high performance, write-scalable Key/Value store, - HTTP/REST via an Apache module - C++ via the NDB API for the lowest absolute latency. Developers can use SQL as well as NoSQL APIs for access to the same data set via multiple query patterns – from simple Primary Key lookups or inserts to complex cross-shard JOINs using Adaptive Query Localization Marrying NoSQL and SQL access to an ACID-compliant database offers developers a number of benefits. MySQL Cluster’s distributed, shared-nothing architecture with auto-sharding and real time performance makes it a great fit for workloads requiring high volume OLTP. Users also get the added flexibility of being able to run real-time analytics across the same OLTP data set for real-time business insight. OK – hopefully you now have a better idea of why ClusterJ and JPA are available. Now, for the Q&A. Q & A Q. Why would I use Connector/J vs. ClusterJ? A. Partly it's a question of whether you prefer to work with SQL (Connector/J) or objects (ClusterJ). Performance of ClusterJ will be better as there is no need to pass through the MySQL Server. A ClusterJ operation can only act on a single table (e.g. no joins) - ClusterJPA extends that capability Q. Can I mix different APIs (ie ClusterJ, Connector/J) in our application for different query types? A. Yes. You can mix and match all of the API types, SQL, JDBC, ODBC, ClusterJ, Memcached, REST, C++. They all access the exact same data in the data nodes. Update through one API and new data is instantly visible to all of the others. Q. How many TCP connections would a SessionFactory instance create for a cluster of 8 data nodes? A. SessionFactory has a connection to the mgmd (management node) but otherwise is just a vehicle to create Sessions. Without using connection pooling, a SessionFactory will have one connection open with each data node. Using optional connection pooling allows multiple connections from the SessionFactory to increase throughput. Q. Can you give details of how Cluster J optimizes sharding to enhance performance of distributed query processing? A. Each data node in a cluster runs a Transaction Coordinator (TC), which begins and ends the transaction, but also serves as a resource to operate on the result rows. While an API node (such as a ClusterJ process) can send queries to any TC/data node, there are performance gains if the TC is where most of the result data is stored. ClusterJ computes the shard (partition) key to choose the data node where the row resides as the TC. Q. What happens if we perform two primary key lookups within the same transaction? Are they sent to the data node in one transaction? A. ClusterJ will send identical PK lookups to the same data node. Q. How is distributed query processing handled by MySQL Cluster ? A. If the data is split between data nodes then all of the information will be transparently combined and passed back to the application. The session will connect to a data node - typically by hashing the primary key - which then interacts with its neighboring nodes to collect the data needed to fulfil the query. Q. Can I use Foreign Keys with MySQL Cluster A. Support for Foreign Keys is included in the MySQL Cluster 7.3 Early Access release Summary The NoSQL Java APIs are packaged with MySQL Cluster, available for download here so feel free to take them for a spin today! Key Resources MySQL Cluster on-line demo  MySQL ClusterJ and JPA On-demand webinar  MySQL ClusterJ and JPA documentation MySQL ClusterJ and JPA whitepaper and tutorial

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  • Tips on googling for sugar

    - by Mikey
    I have a question up on SO I am a little embarassed I can't just google: http://stackoverflow.com/questions/13734664/groovy-variables-in-method-names-with-double-question-marks The problem is google seems to chuck any terms that are just punctuation, so queries like these: .findBy?? .and?? groovy '??' Are coming out the same as these: findBy and groovy I have had this problem before when I didn't know the name of the elvis operator, and countless other times (probably happened first time I saw an infix '%' mod too if I had to guess). Is there a resource for syntax sugar lookups? Some way to force google or a different search engine to not ignore my funky punctuation?

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  • Unity Dash/Lens - Auto-completion based on recent search strings

    - by Anant
    Sometimes, I find myself typing the same (or quite similar) search strings inside a Unity Lens. So, I thought whether it's possible for the Lens to remember previous searches, and provide a drop-down menu of possible suggestions (based on the past) when I start typing my new query. With Lenses for sites like Wikipedia and DuckDuckGo, the search strings are getting longer, and this feature would lend a helping hand in filling out queries faster. This could be something applicable to all Lenses, with later versions allowing individual Lenses to run their own auto-completion algorithm.

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  • Google Webmaster Tools, DNS Errors & HostPapa

    - by Gravy
    Received a message from Google Webmaster Tools: Over the last 24 hours, Googlebot encountered 2 errors while attempting to retrieve DNS information for your site. The overall error rate for DNS queries for your site is 40.0%. You can see more details about these errors in Webmaster Tools. Recommended action Contacted HostPapa and they deny that there is any issue with the site / server!!! Support in terms of what I can do to actually resolve this issue is non-existent!!!! The site is currently online. And I don't know much about DNS... so any advice about what I can do to resolve this problem would be much appreciated. Basically, the message from Google says that it is my webhosts fault, the message from my webhost (HostPapa) is... "Just tell google to crawl your site as there are no errors."

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  • Display root node of Hierarchical Tree using ADF - EJB DC

    - by arul.wilson(at)oracle.com
    Displaying Employee (HR schema) records in Hierarchical Tree can be achieved in ADF-BC by creating custom VO and a Viewlink for displaying root node. This can be more easily done using  EJB-DC by just introducing a NamedQuery to get the root node.Here you go to get this scenario working.Create DB connection based on HR schema.Create Entity Bean from Employees Table.Add custom NamedQuery to Employees.java bean, this named query is responsible for fetching the root node (King in this example). @NamedQueries({  @NamedQuery(name = "Employees.findAll", query = "select o from Employees o"),  @NamedQuery(name = "Employees.findRootEmp", query = "select p from Employees p where p.employees is null")}) Create Stateless Session Bean and expose the Named Queries through the Session Facade.Create Datacontrol from SessionBean local interface.Create jspx page in ViewController project.Drop employeesFindRootEmp from Data Controls Palette as ADF Tree.Add employeesList as Tree level rule.Run page to see the hierarchical tree with root node as 'King'

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